A virtual reality-based training system for error-augmented treatment in patients with stroke

Abstract

Stroke is a leading cause of long-term sensorimotor deficits in upper limb function and current upper limb interventions have limited effectiveness. Joint-level augmentation treatment, grounded in referent control theory, prescribes insertion of error at the joint level for inducing a dynamic re-mapping of muscle-leve control mechanisms. We hypothesize that this will lead to an increase in the control range of the joint and consequently to improved performance of voluntary motion. In the current presentation we describe a system harnessing virtual reality developed for upper-limb training based on joint level error augmentation. The system comprises three components, a passive arm rest supporting the arm against gravity, a Kinect motion tracking system, and a virtual-reality training environment. The visualization of the entire arm is a critical system component which should invoke a high degree of presence. For the method to be effective, the participant should accept the visualized arm position as representing his/her actual arm location, despite conflicting input from his/her proprioception. A pilot test is currently under way for assessing the method’s effectiveness.